MétaCan
Menu
Back to cohort
Record W2334843839 · doi:10.1097/ede.0b013e31826ce65b

Rejoinder

2012· letter· fr· W2334843839 on OpenAlexaff
David W. Dowdy, Madhukar Pai

Bibliographic record

VenueEpidemiology · 2012
Typeletter
Languagefr
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsKnowledge translationPopulationRepresentation (politics)Public relationsPolitical scienceEpistemologyMedicineSociologyLaw and economicsComputer sciencePoliticsKnowledge managementLaw

Abstract

fetched live from OpenAlex

In this issue of EPIDEMIOLOGY, we propose the term “Accountable Health Advocate” (AHA) as “a model for the epidemiologist who specializes in knowledge synthesis, translation, and dissemination, in addition to knowledge generation.”1 The AHA model, like any other model, cannot provide a perfect representation of reality. At best, a model is a useful representation—in this case, one that may help to distill complex phenomena into measures that we can use to improve health. McKeown2 and Ness3 point out that our model of the AHA is far from perfect. We do not disagree. As McKeown describes, the practice of epidemiology is dynamic and multidimensional, and many epidemiologists do indeed practice both knowledge generation and knowledge translation to varying degrees. By highlighting the importance of accountable health advocacy, we wish to neither downplay the importance of the many initiatives cited by McKeown and Ness—from the Plain Writing Act to the Joint Policy Committee of the Societies of Epidemiology—nor suggest that these initiatives lack impact. Rather, as Ness argues, we hope that epidemiologists will expand these and other initiatives that allow us to “move from observation to intervention.” The question is not whether the model of epidemiologist as AHA is “right,” but whether reformulating the epidemiologist as AHA is useful to achieve the ultimate goal identified by Ness as the “attainment of better population health.” In defining population health, we believe the focus should be on human well being,4 not merely the advancement of knowledge. McKeown2 and Ness3 argue that our formulation of the AHA may paradoxically detract from the goal of improved human well being, by allowing “the rest of us”2 to “eschew [our] responsibility”3 to be accountable to society, to prioritize population health, and to actively engage with policymakers and opinion leaders. McKeown and Ness worry that, if we brand some epidemiologists with expertise in knowledge translation as AHAs, others with expertise in knowledge generation may brand themselves as “non-AHAs.” The concern is that such people would, as a result of the AHA model, become less accountable to society, less concerned with population health, and less engaged with decision makers. We do not believe this will happen. Rather, we see the AHA as analogous to the voluntary credentialing of public health graduates, initiated in 2008 as a method of “encouraging recognition of new public health graduates prepared with [a] broad vision of public health.”5,6 Some persons who decline to participate in the credentialing process do so not because they reject the importance of broad expertise in public health, but because the credentialing “square peg” does not fit their “round” professional vision. The success or failure of that initiative can only be measured over time as epidemiologists and society either recognize its value and adopt it more widely or allow it to fall by the wayside. Similarly, we propose the AHA model as a means to encourage epidemiologists who might wish to shift their professional focus more toward knowledge translation but lack the professional support (funding, publication, professional advancement) to do so. Over time, the AHA model will either be further developed and more widely adopted or (if there is no need for a new type of epidemiology) forgotten. We see the AHA model as particularly relevant to students and other trainees of epidemiology, whose professional paths are most malleable. We cannot expect students to fully grasp the nuances of the spectrum of epidemiologic practice before they enter the workforce; a simple conceptual framework can be useful as trainees consider the professional options available to them. For students who plan to pursue methodologically oriented academic training (eg, PhD rather than DrPH) but do not wish to fashion themselves primarily as knowledge generators, the AHA model may provide a roadmap. As Ness3 points out, complacency is the one thing we cannot afford in pursuit of a brand of epidemiology that more universally approaches the AHA ideal. The desire to improve health is an urgent one, and we need alternatives to the status quo. The AHA model does not capture the full scope of epidemiologic work, nor will it fit every epidemiologist’s professional vision. However, we hope that there will be epidemiologists (including trainees) to whom this model speaks, and who will find incentives to shape their practice in the direction of more knowledge translation and ultimately improved human health. Despite being imperfect, we hope the AHA model might ultimately prove useful.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.116
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0090.011
Insufficient payload (model declined to judge)0.0460.043

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.305
GPT teacher head0.540
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueEpidemiologySame topicPublic Health Policies and EducationFrench-language works237,207